Airplane Type Identification Based on Mask RCNN and Drone Images

For dealing with traffic bottlenecks at airports, aircraft object detection is insufficient. Every airport generally has a variety of planes with various physical and technological requirements as well as diverse service requirements. Detecting the presence of new planes will not address all traffic congestion issues. Identifying the type of airplane, on the other hand, will entirely fix the problem because it will offer important information about the plane's technical specifications (i.e., the time it needs to be served and its appropriate place in the airport). Several studies have provided various contributions to address airport traffic jams; however, their ultimate goal was to determine the existence of airplane objects. This paper provides a practical approach to identify the type of airplane in airports depending on the results provided by the airplane detection process using mask region convolution neural network. The key feature employed to identify the type of airplane is the surface area calculated based on the results of airplane detection. The surface area is used to assess the estimated cabin length which is considered as an additional key feature for identifying the airplane type. The length of any detected plane may be calculated by measuring the distance between the detected plane's two furthest points. The suggested approach's performance is assessed using average accuracies and a confusion matrix. The findings show that this method is dependable. This method will greatly aid in the management of airport traffic congestion.

Spatial Fuzzy Clustering on Synthetic Aperture Radar Images to Detect Changes

Data and data sources have become increasingly essential in recent decades. Scientists and researchers require more data to deploy AI approaches as the field continues to improve. In recent years, the rapid technological advancements have had a significant impact on human existence. One major field for collecting data is satellite technology. With the fast development of various satellite sensor equipment, synthetic aperture radar (SAR) images have become an important source of data for a variety of research subjects, including environmental studies, urban studies, coastal extraction, water sources, etc. Change detection and coastline detection are both achieved using SAR pictures. However, speckle noise is a major problem in SAR imaging. Several solutions have been offered to address this issue. One solution is to expose SAR images to spatial fuzzy clustering. Another solution is to separate speech. This study utilises the spatial function to overcome speckle noise and cluster the SAR images with the highest achieved accuracy. The spatial function is proposed in this work since the likelihood of data falling into one cluster is what this function is all about. When the spatial function is employed to cluster data in fuzzy logic, the clustering outcomes improve. The proposed clustering technique is us

Airplane Type Identification Based on Mask RCNN; An Approach to Reduce Airport Traffic Congestion

One of the most difficult jobs in remote sensing is dealing with traffic bottlenecks at airports. This fact has been confirmed by several studies attempting to resolve this issue. Among a wide range of approaches employed the most successful methods have been based on airplane object recognition using satellite images as datasets for deep learning models. Airplane object identification is not a viable method for resolving traffic congestion. There are several types of airplanes at the airport each with its own set of requirements and specifications.Utilizing satellite pictures will require the use of complex equipment which is a financial burden. In this work a universal low-cost and efficient solution for airport traffic congestion is proposed. Drone-captured aerial pictures are used to train and assess a Mask Region Convolution Neural Network model. This model can detect the presence of any aircraft in a photograph and pinpoint its location. It also includes mask estimations to properly identify each detected airplane type based on the estimated surface area and cabin length which are crucial variables in distinguishing planes. The study is conducted using Microsoft Common Object in Context COCO metrics average accuracies and a confusion matrix all of which demonstrate the approach's potential in providing valuable aid for dealing with traffic congestion at airports.